Clogs on High Heels: Dutch Cultural Heritage and Fashion

Teunissen, José

doi:10.5040/9781350986039.ch-002

Cited by 8 publications

(9 citation statements)

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“…In general, data snooping belongs to the family of statistical procedures known as data dredging (together with fishing or hunting) [20]. Considering the vector of observations y, one should start first with the overall test for outliers based on the following null (H 0 ) and a mean shift alternative hypotheses (H A ) [21,24,25]…”

Section: Outlier Detection Data Cleaning Methods and Similar Approachesmentioning

confidence: 99%

Robust procedures in processing measurements in geodesy and surveying: a review

Duchnowski,

Wyszkowska

2024

Meas. Sci. Technol.

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Outlying observations are undesirable but possible elements of geodetic measurements. In such a context, the primary and trivial solution is to repeat “suspected” observations. The question arises: what if the measurements cannot be performed again, or if one cannot flag outliers easily and efficiently? In such a case, one should process data by applying methods that consider the possible occurrence of outlying observations. Historically, except for some previous attempts, the statistical approach to robust estimation originates in the 60s of the 20th century and refers to the pioneer papers of Huber, Tukey, Hampel, Hodges, and Lehmann. Also, the statistical procedures known as data snooping (data dredging) were developed at a similar time. It took not a long time before robust procedures were implemented for processing geodetic observations or adjustment of observation systems. The first works of Baarda and Pope encouraged other scientists or surveyors to elaborate robust procedures adapted for geodetic or surveying problems, which resulted in their rapid development in the last two decades of the 20th century. The question for the 21st century is whether robustness is still an important issue relating to modern measurement technologies and numerical data processing. One should realize that modern geodetic techniques do not decrease the probability of outlier occurrence. Considering measurement systems that yield big data, it is almost certain that outliers occur somewhere. The paper reviews different approaches to robust processing of geodetic observations, from the data snooping methods, random sampling, M-estimation, R-estimation, and Msplit estimation to robust estimation of the variance coefficient. Such a variety reflects different natures, origins, or properties of outliers and the apparent fact that there is no best and most efficient and universal robust approach. The methods presented are indeed the basis for future solutions based on, e.g., machine learning.

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Section: Outlier Detection Data Cleaning Methods and Similar Approachesmentioning

confidence: 99%

Robust procedures in processing measurements in geodesy and surveying: a review

Duchnowski,

Wyszkowska

2024

Meas. Sci. Technol.

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“…We used two statistical tests: (a) the Overall Model Test (OMT) (Teunissen, 2006) and (b) the Akaike Information Criterion (AIC) (Akaike, 1974) to assess the models for goodness of fit considering both data (co)variance and the degrees of freedom for each of the models. Both test statistics were calculated under the assumption that the residuals are normally distributed.…”

Section: Deformation Source Modelingmentioning

confidence: 99%

“…The OMT test uses test statistic T , which is given by the weighted sum of squared residuals:

T={\hat{e}}^{T}{Q}_{yy}^{-1}\hat{e},

where

\hat{e}

are the residuals (

\hat{e}=O-M

, where O are the observations and M is the optimal model solution) and Q yy is the covariance matrix of the observations made using an exponential variogram fitted to the sub‐sampled data. Test statistic T follows a χ 2 ( N df , 0) distribution, where N df is the degrees of freedom of the model (Teunissen, 2006). The critical value

{K}_{{N}_{\text{df}},\alpha }

is given by the inverse cumulative probability density function for a χ 2 ( N df , 0) distribution at the required level of significance α , in our Case 0.05.…”

Section: Deformation Source Modelingmentioning

confidence: 99%

High‐Resolution InSAR Reveals Localized Pre‐Eruptive Deformation Inside the Crater of Agung Volcano, Indonesia

et al. 2023

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Localized deformation at volcanoes is caused by shallow processes including magma movement, pressurization of a hydrothermal system, or instability of the edifice, and is often observed shortly before or during an eruption (Cassidy et al., 2019). Thus, observing localized deformation is critical for understanding the final stages of magma ascent and eruption forecasting. However, sufficiently frequent and dense spatial observations are rarely available from ground-based instrument networks. High-resolution Interferometric Synthetic Aperture Radar (InSAR) can achieve <1 m spatial resolution and sub-weekly observations (e.g., COSMO-Skymed (CSK) (Italiana, 2007), TerraSAR-X (TSX) (Mittermayer et al., 2014), and ICEYE (Ignatenko et al., 2020)), making it an excellent tool for the detection, monitoring, and understanding of localized volcano deformation. For example,

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“…It is important to note that in this example the first and second columns of the matrix 𝑪 were selected, which refer to points A and B, respectively. After postulating the null and alternative hypotheses, the test statistic derived from the concept of the maximum likelihood ratio test are computed as [37]:…”

Section: Null and Alternative Hypothesismentioning

confidence: 99%

A New Sequential Statistical Test Procedure from Observation Differences for Geodetic Deformation Analysis

Matsuoka,

Rofatto,

Assunção

et al. 2023

Preprint

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One of the main challenges in geodetic deformation analysis is to infer whether the geometry of some engineering structures or zones of natural hazards has changed from their initial state or not. The pillar that supports such an analysis is tightly based on the fundamentals of statistical hypothesis testing. The null hypothesis model indicates that no displacement has occurred. It is tested against a class of alternative models, which stipulate different displacement patterns. In this contribution, we present an innovative geodetic displacement detection which integrates combinatorial analysis and likelihood ratio tests into a sequential procedure for the case where the differences between observations of two epochs are in play. This framework is applied and investigated to two test scenarios: a synthetic and a real simulated trilateration network. From a statistical point of view, our approach is rigorous, because the alternative model can identify simultaneously more than one unstable point. In addition, the relationship between the unknown parameters and the observations is always linear, even if the problem manifests itself as non-linear. Consequently, we avoid a potential loss of statistical test power due to the model linearization. In addition, the proposed method controls the false positive rate efficiently. One of the problems that arise here is also related to the selection of the maximum number of points to be considered in the test procedure ( ). Here, we provide an innovative methodology based on rank computation of the design matrices to define , which can even be extended to the problem of outlier detection. Determining avoids the problem of having non-separable models in identifying unstable points. The algorithms and data are available in the repository.

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Clogs on High Heels: Dutch Cultural Heritage and Fashion

Cited by 8 publications

References 0 publications

Robust procedures in processing measurements in geodesy and surveying: a review

Robust procedures in processing measurements in geodesy and surveying: a review

High‐Resolution InSAR Reveals Localized Pre‐Eruptive Deformation Inside the Crater of Agung Volcano, Indonesia

A New Sequential Statistical Test Procedure from Observation Differences for Geodetic Deformation Analysis

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